Approximate Inference Control

author: Marc Toussaint, Machine Learning and Intelligent Data Analysis Group, TU Berlin
published: Jan. 19, 2010,   recorded: December 2009,   views: 4759


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Approximate Inference Control (AICO) is a method for solving Stochastic Optimal Control (SOC) problems. The general idea is to think of control as the problem of computing a posterior over trajectories and control signals conditioned on constraints and goals. Since exact inference is infeasible in realistic scenarios, the key for high-speed planning and control algorithms is the choice of approximations. In this talk I will introduce to the general approach, discuss its intimate relations to DDP and the current research on Kalman's duality, and discuss the approximations that we use to get towards real-time planning in high-dimensional robotic systems. I will also mention recent work on using Expectation Propagation and truncated Gaussians for inference under hard constraints and limits as they typically arise in robotics (collision and joint limit constraints).

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Reviews and comments:

Comment1 Aidin, March 28, 2012 at 1:47 a.m.:

Wow. I really like this topic. I think the main advantage of this is the integration ability of it. I mean, estimation, motion control and high level reasoning can all be formulated in the same framework.

I think the potential for using sample based representations to get more global solutions would be interesting subject for research...

Thanks for the great lecture
Ryerson University

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